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Data Engineer with 1+ years in Python, SQL & GCP Cloud Data Pipelines.
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Assessing your cultural and operational fit
Data Engineer with production experience designing cloud-native data pipelines, automated archival systems, and DAG-based workflow orchestration on GCP using Python, SQL, Apache Airflow, Dataform, and BigQuery. Built and delivered a production archival pipeline processing ~100M rows daily that reduced warehouse storage costs by 40-50% through idempotent batch processing, automated data quality validation, self-healing recovery, and structured operational observability. Contributed SQL logic and dimensional modeling inputs to Dataform ELT workflows incorporating SCD Type 2 change tracking, surrogate key generation, and structured audit logging.
Neil Gogte Institute of Technology, Hyderabad
B.E. · Information Technology
N/A – June 30, 2023
Fariz Infosolutions Pvt. Ltd. (FISClouds)
Associate Data Engineer
January 1, 2025 – Present
Hyderābād, Telangana, India
BigQuery Partition Archival & Recovery Pipeline
June 18, 2026 – Present
• Built a production-grade archival system implementing hot-to-cold storage tiering – migrating aging BigQuery partitions to GCS cold storage before TTL expiry, with multi-stage data quality validation and idempotent recovery safeguards to prevent data loss. • Designed a metadata-driven, configuration-first architecture: onboarding new tables requires only config updates with zero code changes, enabling self-service pipeline extension at scale. • Implemented automated partition backfill logic that scans partitions nearing expiry and queues missing archives for rerun, ensuring complete data retention compliance without manual intervention. • Maintained a structured audit trail and data lineage log covering archival runs, validation outcomes, and purge events for full operational traceability. • Optimized orchestration by invoking the BigQuery client directly for exports, bypassing heavier Airflow operator abstractions to reduce per-partition execution latency.
Google Cloud Certified - Professional Data Engineer
Google Cloud
August 1, 2025 – August 1, 2027
Cultural Fit Analysis
The candidate's experience with cloud-native data pipelines, automated systems, and data quality frameworks aligns well with modern data engineering practices. The focus on cost optimization and operational efficiency suggests a results-oriented mindset. The breadth of skills across GCP services, data modeling, and orchestration tools indicates adaptability and a willingness to learn and apply diverse technologies.
Soft Skills & Operational Fit
The candidate's project and experience descriptions highlight a strong focus on building robust, self-healing, and observable systems, which indicates good problem-solving skills, attention to detail, and a proactive approach to operational excellence. The emphasis on metadata-driven architecture and self-service pipeline extension suggests an understanding of scalability and maintainability.